001041671 001__ 1041671
001041671 005__ 20250505202224.0
001041671 037__ $$aFZJ-2025-02376
001041671 041__ $$aEnglish
001041671 1001_ $$0P:(DE-Juel1)178725$$aBouss, Peter$$b0$$eCorresponding author$$ufzj
001041671 1112_ $$aDPG Spring Meeting of the Condensed Matter Section$$cRegensburg$$d2025-03-16 - 2025-03-21$$wGermany
001041671 245__ $$aEmploying normalizing flows to examine neural manifold characteristics and curvatures
001041671 260__ $$c2025
001041671 3367_ $$033$$2EndNote$$aConference Paper
001041671 3367_ $$2DataCite$$aOther
001041671 3367_ $$2BibTeX$$aINPROCEEDINGS
001041671 3367_ $$2DRIVER$$aconferenceObject
001041671 3367_ $$2ORCID$$aLECTURE_SPEECH
001041671 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1746441693_14470$$xAfter Call
001041671 520__ $$aDespite the vast number of active neurons, neuronal population activity supposedly lies on low-dimensional manifolds (Gallego et al., 2017). To learn the statistics of neural activity, we use Normalizing Flows (NFs) (Dinh et al., 2014). These neural networks are trained to estimate the probability distribution by learning an invertible map to a latent distribution.We adjust NF’s training objectives to distinguish between relevant and noise dimensions, by using a nested dropout procedure in the latent space (Bekasov & Murray, 2020). An approximation of the network for each mixture component as a quadratic mapping enables us to calculate the Riemannian curvature tensors of the neural manifold. We focus mainly on the directions in the tangent space, in which the sectional curvature shows local extrema.Finally, we apply the method to electrophysiological recordings of the visual cortex in macaques (Chen et al., 2022). We show that manifolds deviate significantly from being flat. Analyzing the curvature of the manifolds yields insights into the regimes where neuron groups interact in a non-linear manner.
001041671 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001041671 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001041671 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x2
001041671 536__ $$0G:(DE-Juel-1)BMBF-01IS19077A$$aRenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A)$$cBMBF-01IS19077A$$x3
001041671 7001_ $$0P:(DE-HGF)0$$aNestler, Sandra$$b1
001041671 7001_ $$0P:(DE-Juel1)180150$$aFischer, Kirsten$$b2$$ufzj
001041671 7001_ $$0P:(DE-HGF)0$$aMerger, Claudia Lioba$$b3
001041671 7001_ $$0P:(DE-HGF)0$$aRene, Alexandre$$b4
001041671 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b5$$eLast author$$ufzj
001041671 8564_ $$uhttps://www.dpg-verhandlungen.de/year/2025/conference/regensburg/part/soe/session/7/contribution/9
001041671 909CO $$ooai:juser.fz-juelich.de:1041671$$pVDB
001041671 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178725$$aForschungszentrum Jülich$$b0$$kFZJ
001041671 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)178725$$aRWTH Aachen$$b0$$kRWTH
001041671 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aTechnion, Haifa, Israel$$b1
001041671 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180150$$aForschungszentrum Jülich$$b2$$kFZJ
001041671 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)180150$$aRWTH Aachen$$b2$$kRWTH
001041671 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a SISSA, Trieste, Italy$$b3
001041671 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b4$$kRWTH
001041671 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Ottawa, Canada$$b4
001041671 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b5$$kFZJ
001041671 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)144806$$aRWTH Aachen$$b5$$kRWTH
001041671 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001041671 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
001041671 9141_ $$y2025
001041671 920__ $$lyes
001041671 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001041671 980__ $$aconf
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001041671 980__ $$aI:(DE-Juel1)IAS-6-20130828
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